What You’ll Do: ⚙️ Build, train, and optimize Machine Learning models & RAG (Retrieval-Augmented Generation) pipelines. 🧠 Develop AI-driven solutions using LangChain or Microsoft Autogen. 🤝 Collaborate with teams to integrate GenAI features into real-world applications. 🔍 Research and explore LLMs (Large Language Models), prompt engineering, and agentic frameworks. 💻 Write clean, efficient, and reusable Python code for AI workflows. What We’re Looking For: 🎓 Pursuing/completed B.Tech/M.Tech in AI/ML, Computer Science, or related fields. 🔗 Hands-on experience with LangChain or Microsoft Autogen (agentic frameworks). 🐍 Strong Python programming skills. 🧪 Basic understanding of TensorFlow or PyTorch. 📊 Experience in building ML models or AI-driven projects.

About teamleaseregtech

TeamLease RegTech, a TeamLease Company, is India's leading Regulatory Technology (RegTech) solutions company enabling Ease of Doing Business for over 1,557 legal entities across 28 States and 8 Union Territories. TeamLease RegTech offers state of the art multi-tenant, SAAS solution on its web and mobile platforms, enabling a transparent, accountable and efficient risk and compliance management. TeamLease RegTech has one of the most comprehensive Legal and Compliance database in India with over 1,536 Acts and 69,233 Compliances updated on a near real time basis covering over 3,000 changes every year. It helps Directors, Board Members and Legal and compliance officers stay up to date on their compliance requirements and reduce operational, reputational and financial risks. Its solutions helps enhance corporate governance for over 1,000 Enterprise and MSME employers by enabling digitisation. TeamLease RegTech serves over 11,485 corporate users located in over 12,000 plants / office / warehouse locations, tracks 2,00,000 compliances and supports filing up of 12,000 Registers, Returns, Challans and other Compliance documents every month. The company envisions a cashless, paperless, presence-less and a simplified compliance administration as the building blocks of EODB 3.0